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      Global waves synchronize the brain’s functional systems with fluctuating arousal

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          Abstract

          Traveling waves spatiotemporally organize brain-wide activity in synchrony with ongoing arousal fluctuations.

          Abstract

          We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure (“functional connectivity”) of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.

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          Most cited references140

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          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              FreeSurfer.

              FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                July 2021
                21 July 2021
                : 7
                : 30
                : eabf2709
                Affiliations
                [1 ]Department of Radiology, Washington University, St. Louis, MO 63110, USA.
                [2 ]Department of Neurology, Washington University, St. Louis, MO 63110, USA.
                [3 ]Department of Psychiatry, Stanford University, Stanford, CA 94305, USA.
                [4 ]Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel.
                [5 ]Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan.
                Author notes
                [* ]Corresponding author. Email: raut@ 123456wustl.edu
                Author information
                http://orcid.org/0000-0002-8761-1431
                http://orcid.org/0000-0002-3379-9627
                http://orcid.org/0000-0002-7520-8581
                http://orcid.org/0000-0003-1087-2884
                http://orcid.org/0000-0002-2869-680X
                http://orcid.org/0000-0002-1847-9588
                Article
                abf2709
                10.1126/sciadv.abf2709
                8294763
                34290088
                a33694b4-438b-4429-992d-1e1692fc3b0a
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 14 October 2020
                : 04 June 2021
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DGE-1745038
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NS080675
                Funded by: doi http://dx.doi.org/10.13039/501100012614, Wuzhou University;
                Award ID: 1U54MH091657
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Neuroscience
                Neuroscience
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                Mjoy Azul

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